Novel adsorbents with a simple preparation process and large capacity for removing highly toxic and nondegradable heavy metals from water have drawn the attention of researchers. Electrospun nanofiber membranes usually have the advantages of large specific surface areas and high porosity and allowing flexible control and easy functionalization. These membranes show remarkable application potential in the field of heavy metal wastewater treatment. In this paper, the electrospinning technologies, process types, and the structures and types of nanofibers that can be prepared are reviewed, and the relationships among process, structure and properties are discussed. On one hand, based on the different components of electrospun nanofibers, the use of organic, inorganic and organic−inorganic nanofiber membrane adsorbents in heavy metal wastewater treatment are introduced, and their advantages and future development are summarized and prospected. On the other hand, based on the microstructure and overall structure of the nanofiber membrane, the recent progresses of electrospun functional membranes for heavy metal removal are reviewed, and the advantages of different structures for applications are concluded. Overall, this study lays the foundation for future research aiming to provide more novel structured adsorbents.

Particle size governs the electronic and geometric structure of metal nanoparticles (NPs), shaping their catalytic performances in heterogeneous catalysis. However, precisely controlling the size of active metal NPs and thereafter their catalytic activities remain an affordable challenge in ultra-deep oxidative desulfurization (ODS) field. Herein, a series of highly-efficient VO_x/boron nitride nanosheets (BNNS)@TiO₂ heterostructures, therein, cetyltrimethylammonium bromide cationic surfactants serving as intercalation agent, BNNS and MXene as precursors, with various VO_x NP sizes were designed and controllably constructed by a facile intercalation confinement strategy. The properties and structures of the prepared catalysts were systematically characterized by different technical methods, and their catalytic activities were investigated for aerobic ODS of dibenzothiophene (DBT). The results show that the size of VO_x NPs and V⁵⁺/V⁴⁺ play decisive roles in the catalytic aerobic ODS of VO_x/BNNS@TiO₂ catalysts and that VO_x/BNNS@TiO₂-2 exhibits the highest ODS activity with 93.7% DBT conversion within 60 min under the reaction temperature of 130 °C and oxygen flow rate of 200 mL·min^–1, which is due to its optimal VO_x dispersion, excellent reducibility and abundant active species. Therefore, the finding here may contribute to the fundamental understanding of structure-activity in ultra-deep ODS and inspire the advancement of highly-efficient catalyst.

The practical application of silica aerogels is an enormous challenge due to the difficulties in improving both mechanical property and thermal insulation performance. In this work, silk fibroin was used as scaffold to improve the mechanical property and thermal insulation performance of silica aerogels. The ungelled SiO₂ precursor solution was impregnated into silk fibroin to prepare silk fibroin–SiO₂ composite aerogels via sol−gel method followed by freeze-drying. By virtue of the interfacial hydrogen-bonding interactions and chemical reactions between silk fibroin and silica nanoparticles, SiO₂ was well-dispersed in the silk fibroin aerogel and composite aerogels exhibited enhanced mechanical property. By increasing the loading of silk fibroin from 15 wt % to 21 wt %, the maximum compressive stress was enhanced from 0.266 to 0.508 MPa when the strain reached 50%. The thermal insulation performance of the composite aerogels was improved compared with pure silica aerogel, as evidenced that the thermal conductivity was decreased from 0.0668 to 0.0341 W∙m^‒1∙K^‒1. Moreover, the composite aerogels exhibited better hydrophobicity and fire retardancy compared to pure silica aerogel. Our work provides a novel approach to preparing silk fibroin–SiO₂ composite aerogels with enhanced mechanical property and thermal insulation performance, which has potential application as thermal insulation material.

Herein, a reversible pH fluorescent sensor was developed using caffeic acid as the precursor by one-step solvothermal synthesis method. The carbon dots-based sensor (CA-CDs) exhibited pH-dependent increase in fluorescence intensity and showed linear relationship in the range of pH 6.60 and 8.00. Notably, the fluorescence sensor has a reversible response to pH change. Finally, the CA-CDs has been successfully applied for two-photon imaging of the pH in liver and kidney of diabetic mice. Imaging results showed that the pH value in kidney of diabetic mice was lower than that of the normal mice, while the pH value in liver of diabetic mice was almost the same as that of the normal mice. The present study provides a simple analytical method for pH detection suitable for in vivo.

The storage and controlled release of singlet oxygen (¹O₂) have attracted increasing attention due to the wide application and microsecond lifetime of ¹O₂ in water. Herein we provide an integrated nanoplatform consisting of a diphenylanthracene derivative, a water-soluble pillar[5]arene and a photosensitizer tetrakis(4-hydroxyphenyl)porphyrin (TPP), that may provide the controlled generation, storage and release of singlet oxygen. We design a new diphenylanthracene derivative with two trimethylammonium bromide groups on both ends that can be well recognized by the pillar[5]arene. The formed nanocarriers can be used to load TPP through their supramolecular self-assembly. The resulting nanoparticles show good water-solubility and uniform spherical morphology. After laser irradiation (660 nm), the nanoparticles exhibit excellent ability for the generation and storage of ¹O₂. When the irradiated nanoparticles are heated above 80 °C, ¹O₂ can be released from the system. Therefore, in this paper we pioneer the use of noncovalent interaction to integrate the diphenylanthracene derivatives and photosensitizers into one functional system, which provides a new strategy for the controlled generation, storage and release of singlet oxygen. We believe this groundbreaking strategy will have a great potential in providing necessary amounts of ¹O₂ for the photodynamic therapy of tumors in dark.

Acceptorless alcohol dehydrogenation stands out as one of the most promising strategies in hydrogen storage technologies. Among various catalytic systems for this reaction, cost-effective molecular catalysts using phosphine-free ligands have gained considerable attention. However, the central challenge for using non-precious metals is to overcome the propensity of reacting by one-electron pathway. Herein, we synthesized a phosphine-free η⁵-C₅Me₅-Co complex by using the metal–ligand cooperative strategy and compared its activity with analogous catalysts toward acceptorless alcohol dehydrogenation. The catalyst showed excellent performance with a turnover number of 130.4 and a selectivity close to 100%. The improved performance among the class of η⁵-C₅Me₅-Co complexes could be attributed to the more accessible Co center and its cooperation with the redox-active ligand. To further study the systematic structure-activity relationship, we investigated the electronic structures of η⁵-C₅Me₅-Co complexes by a set of characterizations. The results showed that the redox-active ligand has a significant influence on the η⁵-C₅Me₅-Co moiety. In the meantime, the proximal O⁻/OH group is beneficial for shuttling protons. For the catalytic cycle, two dehydrogenation scenarios were interrogated through density functional theory, and the result suggested that the outer-sphere pathway was preferred. The formation of a dihydrogen complex was the rate-determining step with a ΔG value of 16.9 kcal∙mol^‒1. The electron population demonstrated that the η⁵-C₅Me₅ ligand played a key role in stabilizing transition states during dehydrogenation steps. This work identified the roles of vital ligand components to boost catalytic performance and offered rationales for designing metal–ligand cooperative nonprecious metal complexes.

Currently, graphene is only considered as a conductive additive and expansion inhibitor in oxides/graphene composite anodes. In this study, a new graphene role (oxygen vacancy inducer) in graphene/oxides composites anodes, which are treated at high-temperature, is proposed and verified using experiments and density functional theory calculations. During high-temperature processing, graphene forms carbon vacancies due to increased thermal vibration, and the carbon vacancies capture oxygen atoms, facilitating the formation of oxygen vacancies in oxides. Moreover, the induced oxygen vacancy concentrations can be regulated by sintering temperatures, and the behavior is unaffected by oxide crystal structures (crystalline and amorphous) and morphology (size and shape). According to density functional theory calculations and electrochemical measurements, the oxygen vacancies enhance the lithium-ion storage performance. The findings can result in a better understanding of graphene’s roles in graphene/oxide composite anodes, and provide a new method for designing high-performance oxide anodes.

The release and control of sulfur species in the pyrolysis of fossil fuels and solid wastes have attracted attention worldwide. Particularly, thiophene derivatives are important intermediates for the sulfur gas release from organic sulfur, but the underlying migration mechanisms remain unclear. Herein, the mechanism of sulfur migration during the release of sulfur-containing radicals in benzothiophene pyrolysis was explored through quantum chemistry modeling. The C₁-to-C₂ H-transfer has the lowest energy barrier of 269.9 kJ·mol^–1 and the highest rate constant at low temperatures, while the elevated temperature is beneficial for C−S bond homolysis. 2-Ethynylbenzenethiol is the key intermediate for the formation of S and SH radicals with the overall energy barriers of 408.0 and 498.7 kJ·mol^–1 in favorable pathways. The generation of CS radicals is relatively difficult because of the high energy barrier (551.8 kJ·mol^–1). However, it can be significantly promoted by high temperatures, where the rate constant exceeds that for S radical generation above 930 °C. Consequently, the strong competitiveness of S and SH radicals results in abundant H₂S during benzothiophene pyrolysis, and the high temperature is more beneficial for CS₂ generation from CS radicals. This study lays a foundation for elucidating sulfur migration mechanisms and furthering the development of pyrolysis techniques.

Reverse-selective membranes have attracted considerable interest for bioethanol production. However, to date, the reverse-separation performance of ethanol/water is poor and the separation mechanism is unclear. Graphene-based membranes with tunable apertures and functional groups have shown substantial potential for use in molecular separation. Using molecular dynamics simulations, for the first time, we reveal two-way selectivity in ethanol/water separation through functional graphene membranes. Pristine graphene (PG) exhibits reverse-selective behavior with higher ethanol fluxes than water, resulting from the preferential adsorption for ethanol. Color flow mappings show that this ethanol-permselective process is initiated by the presence of ethanol-enriched and water-barren pores; this has not been reported in previous studies. In contrast, water molecules are preferred for hydroxylated graphene membranes because of the synergistic effects of molecular sieving and functional-group attraction. A simulation of the operando condition shows that the PG membrane with an aperture size of 3.8 Å achieves good separation performance, with an ethanol/water separation factor of 34 and a flux value of 69.3 kg∙m^‒2∙h^‒1∙bar^‒1. This study provides new insights into the reverse-selective mechanism of porous graphene membranes and a new avenue for efficient biofuel production.

Corn to sugar process has long faced the risks of high energy consumption and thin profits. However, it’s hard to upgrade or optimize the process based on mechanism unit operation models due to the high complexity of the related processes. Big data technology provides a promising solution as its ability to turn huge amounts of data into insights for operational decisions. In this paper, a neural network-based production process modeling and variable importance analysis approach is proposed for corn to sugar processes, which contains data preprocessing, dimensionality reduction, multilayer perceptron/convolutional neural network/recurrent neural network based modeling and extended weights connection method. In the established model, dextrose equivalent value is selected as the output, and 654 sites from the DCS system are selected as the inputs. LASSO analysis is first applied to reduce the data dimension to 155, then the inputs are dimensionalized to 50 by means of genetic algorithm optimization. Ultimately, variable importance analysis is carried out by the extended weight connection method, and 20 of the most important sites are selected for each neural network. The results indicate that the multilayer perceptron and recurrent neural network models have a relative error of less than 0.1%, which have a better prediction result than other models, and the 20 most important sites selected have better explicable performance. The major contributions derived from this work are of significant aid in process simulation model with high accuracy and process optimization based on the selected most important sites to maintain high quality and stable production for corn to sugar processes.